{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "large-facing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设备id 设备三设置参数 设备参数 \n",
    "import math\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "killing-pittsburgh",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 适应函数代码\n",
    "\n",
    "def funShunbert(x):\n",
    "    #计算粒子适应度值\n",
    "    h1 = 0\n",
    "    h2 = 0\n",
    "    for i in range(5):\n",
    "        h1 = h1+i*math.cos((i+1)*x[0]+i)\n",
    "        h2 = h2+i*math.cos((i+1)*x[1]+i)\n",
    "    y = h1*h2\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "three-miracle",
   "metadata": {},
   "outputs": [],
   "source": [
    "#参数初始化\n",
    "c1 ,c2= 1.4, 1.5 #加速度因子\n",
    "maxgen = 500 #进化次数\n",
    "sizepop = 50 #种族规模\n",
    "w = 0.8 #惯性权重\n",
    "Vmax, Vmin =5,-5 #速度最大值，速度最小值\n",
    "popmax, popmin = 10, -10 #个题最大值，个体最小值\n",
    "pop = np.zeros((sizepop,2))#种群\n",
    "V = np.zeros((sizepop,2)) #速度\n",
    "fitness = np.zeros((sizepop,1))#适应度\n",
    "trace = np.zeros((maxgen,1))#结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "amazing-argentina",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Rands(m,n):\n",
    "    return np.random.uniform(-1,1,(m,n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "laden-vocabulary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.89546569,  0.74198634])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rands(1,2)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "willing-today",
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in range(sizepop):\n",
    "    pop[j,:] = 5 * Rands(1,2)[0]\n",
    "    V[j,:] = Rands(1,2)[0]\n",
    "    fitness[j] = funShunbert(pop[j,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "adjacent-russell",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-18.33317144] 35\n"
     ]
    }
   ],
   "source": [
    "bestindex = np.argmin(fitness)\n",
    "bestfitness = fitness[bestindex]\n",
    "print(bestfitness, bestindex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "immune-matrix",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.82153571 -0.9335467 ]\n"
     ]
    }
   ],
   "source": [
    "Gbest = pop[bestindex,:]\n",
    "print(Gbest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "departmental-weekly",
   "metadata": {},
   "outputs": [],
   "source": [
    "fitnessGbest = bestfitness\n",
    "Pbest = pop\n",
    "fitnessPbest = fitness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "desperate-barbados",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitnessPbest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "marine-pound",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-70.83653061]\n",
      "[-71.9665315]\n",
      "[-77.9805836]\n",
      "[-15.72559102]\n",
      "[-77.36157575]\n",
      "[-23.6163875]\n",
      "[-21.15000565]\n",
      "[-72.59753967]\n",
      "[-29.24740963]\n",
      "[-47.78247279]\n",
      "[-54.86403646]\n",
      "[-56.08665191]\n",
      "[-21.38751077]\n",
      "[-70.06759233]\n",
      "[-42.4260427]\n",
      "[0.86367641]\n",
      "[-3.55853684]\n",
      "[-38.63759103]\n",
      "[-43.3502963]\n",
      "[-82.51249357]\n",
      "[-54.96326738]\n",
      "[-26.01798031]\n",
      "[-65.12170153]\n",
      "[-55.71282532]\n",
      "[-25.4962379]\n",
      "[-84.28185572]\n",
      "[-23.22872373]\n",
      "[-53.20606184]\n",
      "[-56.98492848]\n",
      "[-29.70972833]\n",
      "[-47.65615167]\n",
      "[-47.43852504]\n",
      "[-47.2643827]\n",
      "[-43.90810985]\n",
      "[-45.18848655]\n",
      "[-57.31181752]\n",
      "[-57.34851183]\n",
      "[-43.86984368]\n",
      "[-10.86912139]\n",
      "[-78.25316915]\n",
      "[-20.70090791]\n",
      "[-64.76300298]\n",
      "[-10.73462856]\n",
      "[-77.8329562]\n",
      "[-46.81366201]\n",
      "[-57.6489241]\n",
      "[-63.55935019]\n",
      "[-23.45768677]\n",
      "[-84.69877411]\n",
      "[-1.49326243]\n",
      "[-38.05472002]\n",
      "[-45.89740937]\n",
      "[-77.00225551]\n",
      "[-45.98022292]\n",
      "[-80.90673579]\n",
      "[-41.74505842]\n",
      "[-53.28804838]\n",
      "[-42.29144778]\n",
      "[-45.64918057]\n",
      "[-18.24509491]\n",
      "[-24.83978021]\n",
      "[-70.44808745]\n",
      "[-78.67737544]\n",
      "[-35.05213653]\n",
      "[-81.05816331]\n",
      "[-56.11914067]\n",
      "[-77.10714648]\n",
      "[-79.27919706]\n",
      "[-51.6773434]\n",
      "[-74.20475998]\n",
      "[-16.0233843]\n",
      "[-81.06901419]\n",
      "[-71.41819135]\n",
      "[-39.01859139]\n",
      "[-82.91577784]\n",
      "[-58.70518713]\n",
      "[-78.96947097]\n",
      "[-16.70294566]\n",
      "[-83.77303391]\n",
      "[-65.89475575]\n",
      "[-13.90163812]\n",
      "[-56.76275805]\n",
      "[-53.67833391]\n",
      "[-66.92204033]\n",
      "[-75.13797076]\n",
      "[-33.81904216]\n",
      "[-37.61496014]\n",
      "[-38.37240301]\n",
      "[-48.24122721]\n",
      "[-51.89973472]\n",
      "[-13.40063767]\n",
      "[-61.44124901]\n",
      "[-57.40267241]\n",
      "[-68.6460505]\n",
      "[-5.51457976]\n",
      "[-32.96877406]\n",
      "[-44.3079488]\n",
      "[-3.07168448]\n",
      "[-49.02441962]\n",
      "[-4.28311146]\n",
      "[-73.59480394]\n",
      "[-78.4606977]\n",
      "[-26.13358174]\n",
      "[-35.11701476]\n",
      "[-47.1858699]\n",
      "[-75.60700668]\n",
      "[-64.57609544]\n",
      "[-14.77700943]\n",
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      "[-15.13986112]\n",
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      "[-2.90670786]\n",
      "[-74.68869324]\n",
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      "[-25.49212542]\n",
      "[-47.41521532]\n",
      "[-49.17679134]\n",
      "[5.98562875]\n",
      "[-66.57689183]\n",
      "[-2.52201139]\n",
      "[-49.89130711]\n",
      "[-0.69600599]\n",
      "[-38.71558759]\n",
      "[-23.89463237]\n",
      "[-83.15182092]\n",
      "[-48.1037477]\n",
      "[-49.96475362]\n",
      "[-52.12090316]\n",
      "[-24.40582782]\n",
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      "[-57.60966722]\n",
      "[-18.49151842]\n",
      "[-39.1821985]\n",
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      "[-78.46243576]\n",
      "[-80.8516463]\n",
      "[-58.610131]\n",
      "[-41.83693913]\n",
      "[-43.18248305]\n",
      "[-67.66422712]\n",
      "[-21.84102579]\n",
      "[-67.50028848]\n",
      "[-24.34648387]\n",
      "[-30.22955742]\n",
      "[-58.10708795]\n",
      "[-11.47061157]\n",
      "[-40.69617389]\n",
      "[-58.04305453]\n",
      "[-24.08960476]\n",
      "[-56.07502491]\n",
      "[-53.68960811]\n",
      "[-8.43659088]\n",
      "[-38.71687121]\n",
      "[-39.48636999]\n",
      "[-44.27501832]\n",
      "[-34.32895399]\n",
      "[-67.36081397]\n",
      "[-61.64599622]\n",
      "[-22.03417716]\n",
      "[-2.5374396]\n",
      "[-37.49126017]\n",
      "[-48.15312887]\n",
      "[-78.91283722]\n",
      "[-65.37966494]\n",
      "[-42.12310886]\n",
      "[-62.03536689]\n",
      "[-61.83204428]\n",
      "[-27.67326475]\n",
      "[-80.80669536]\n",
      "[-55.51235555]\n",
      "[-58.51626545]\n",
      "[-19.02394957]\n",
      "[-21.35023795]\n",
      "[-22.88319896]\n",
      "[-51.10066436]\n",
      "[-22.22723722]\n",
      "[-55.41926328]\n",
      "[-79.1423708]\n",
      "[-84.38404599]\n",
      "[-56.9544396]\n",
      "[-51.08403322]\n",
      "[-9.44821333]\n",
      "[-58.27012739]\n",
      "[-20.94964262]\n",
      "[-23.01600417]\n",
      "[-51.72258735]\n",
      "[-41.83476336]\n",
      "[-76.11034219]\n",
      "[-40.3656413]\n",
      "[-57.33437631]\n",
      "[-41.47966133]\n",
      "[-57.2726409]\n",
      "[-70.81064244]\n",
      "[-44.50987942]\n",
      "[-15.99943083]\n",
      "[-50.16971268]\n",
      "[-32.82180729]\n",
      "[-50.72329144]\n",
      "[-19.73885366]\n",
      "[-23.39739842]\n",
      "[-50.9705582]\n",
      "[-1.46591809]\n",
      "[-21.5168793]\n",
      "[-29.41383953]\n",
      "[-32.91760742]\n",
      "[-40.69336979]\n",
      "[-6.84210826]\n",
      "[-48.83859457]\n",
      "[-72.11207138]\n",
      "[-42.69071001]\n",
      "[-57.85636322]\n",
      "[-2.66003288]\n",
      "[-35.67172136]\n",
      "[-15.42007774]\n",
      "[-32.53008795]\n",
      "[-52.46084147]\n",
      "[-21.55331099]\n",
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      "[-45.42626557]\n",
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      "[3.65887097]\n",
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      "[-62.41263725]\n",
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      "[-60.90538468]\n",
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      "[-80.64983285]\n",
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      "[-60.5253237]\n",
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      "[-67.3003588]\n",
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      "[-47.08241688]\n",
      "[-57.55028527]\n",
      "[-81.18497328]\n",
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      "[-83.66205157]\n",
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      "[-47.5141591]\n",
      "[-4.02169998]\n",
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      "[-6.08074421]\n",
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      "[-56.65291247]\n",
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      "[-53.27185368]\n",
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      "[-22.52193413]\n",
      "[-10.91266912]\n",
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      "[-20.2197851]\n",
      "[-58.20940212]\n",
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      "[-31.85197177]\n",
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      "[-11.58633174]\n",
      "[-41.63173114]\n",
      "[-7.48820865]\n",
      "[-35.09970906]\n",
      "[-49.84838743]\n",
      "[-30.32729184]\n",
      "[-52.47553735]\n",
      "[-18.83292733]\n",
      "[-45.19429914]\n",
      "[-83.07018018]\n",
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      "[-61.07697579]\n",
      "[-80.45504393]\n",
      "[-16.76673848]\n",
      "[-34.68335962]\n",
      "[-37.26432999]\n",
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      "[-81.79076429]\n",
      "[-26.92014227]\n",
      "[-51.64912343]\n",
      "[-54.09869171]\n",
      "[-78.4371275]\n",
      "[-5.81685293]\n",
      "[-80.59261138]\n",
      "[-39.21133647]\n",
      "[-72.90091358]\n",
      "[-41.21760132]\n",
      "[-60.9308373]\n",
      "[-55.50639824]\n",
      "[-35.12984319]\n",
      "[-50.74621259]\n",
      "[-45.36470837]\n",
      "[-71.2057348]\n",
      "[-46.09338488]\n",
      "[-57.48171705]\n",
      "[-11.70919842]\n",
      "[-34.78216786]\n",
      "[-57.30057861]\n",
      "[-66.64106876]\n",
      "[-15.68610172]\n",
      "[-73.61147006]\n",
      "[-75.97070948]\n",
      "[-16.7621214]\n",
      "[-49.40581293]\n",
      "[-52.5997391]\n",
      "[-70.73103611]\n",
      "[-0.97321054]\n",
      "[-10.8804128]\n",
      "[-11.55886639]\n",
      "[-46.22019124]\n",
      "[0.24593076]\n",
      "[-56.94600059]\n",
      "[-58.35479255]\n",
      "[-39.62407052]\n",
      "[-54.31400836]\n",
      "[-22.6108295]\n",
      "[-21.95724264]\n",
      "[-57.5861586]\n",
      "[-10.34368035]\n",
      "[-54.38149712]\n",
      "[-19.78396457]\n",
      "[-69.89001906]\n",
      "[-32.98218567]\n",
      "[-36.67641907]\n",
      "[-46.06903543]\n",
      "[-0.85026186]\n",
      "[-42.0599258]\n",
      "[-10.9903282]\n",
      "[-27.66735797]\n",
      "[-33.44885009]\n",
      "[-33.93506673]\n",
      "[-38.3256395]\n",
      "[-54.57713946]\n",
      "[-69.64270454]\n",
      "[-39.24717705]\n",
      "[-57.68962337]\n",
      "[-72.38601272]\n",
      "[-59.23668687]\n",
      "[0.28215428]\n",
      "[-32.67607722]\n",
      "[-9.5642523]\n",
      "[-40.41222473]\n",
      "[-47.70076726]\n",
      "[-79.20018602]\n",
      "[0.13614383]\n",
      "[-55.01216471]\n",
      "[-50.67674177]\n",
      "[-0.17707897]\n",
      "[-76.20356724]\n",
      "[-53.02906844]\n",
      "[-58.04093027]\n",
      "[-74.72501379]\n",
      "[-15.7550296]\n",
      "[-65.1476999]\n",
      "[-21.76561199]\n",
      "[-68.28472401]\n",
      "[-80.54546059]\n",
      "[-82.56002927]\n",
      "[-21.68084797]\n",
      "[-56.13171785]\n",
      "[-38.0808574]\n",
      "[-22.70837414]\n",
      "[-64.31422532]\n",
      "[-18.70502191]\n",
      "[-52.04654895]\n",
      "[-78.32927108]\n",
      "[-48.87485202]\n",
      "[-45.04671586]\n",
      "[-54.0556132]\n",
      "[-37.24490433]\n",
      "[-43.07210706]\n",
      "[-57.2687914]\n",
      "[-6.3444412]\n",
      "[-54.82012676]\n",
      "[-33.23864428]\n",
      "[-56.71723311]\n",
      "[-47.86997598]\n",
      "[-71.50781963]\n",
      "[-81.48180705]\n",
      "[-36.63196349]\n",
      "[-61.66761388]\n",
      "[-75.32492437]\n",
      "[-73.91005596]\n",
      "[-36.73924494]\n",
      "[-52.81022088]\n",
      "[-16.10009253]\n",
      "[-62.04322852]\n"
     ]
    }
   ],
   "source": [
    "for i in range(maxgen):\n",
    "    for j in range(sizepop):\n",
    "      \n",
    "        V[j,:] = w*V[j,:] + c1*random.uniform(0,1)*(Pbest[j,:]-pop[j,:])+c2*random.uniform(0,1)*(Gbest-pop[j,:])\n",
    "        V[j,V[j,:]>Vmax] = Vmax\n",
    "        V[j,V[j,:]<Vmin] = Vmin\n",
    "        pop[j,:] =  pop[j,:]+ V[j,:]\n",
    "        \n",
    "        pop[j,pop[j,:]>popmax] = popmax\n",
    "        pop[j,pop[j,:]<popmin] = popmin\n",
    "        \n",
    "        if random.uniform(0,1) >0.9:\n",
    "            pop[j,:]= Rands(1,2)[0]\n",
    "            \n",
    "        fitness[j]=funShunbert(pop[j,:])\n",
    "        \n",
    "\n",
    "   \n",
    "        if fitness[j] < fitnessPbest[j]:\n",
    "            Pbest[j,:]=pop[j,:]\n",
    "            fitnessPbest[j]=fitness[j]\n",
    "            \n",
    "        if fitness[j] < fitnessGbest:\n",
    "            Gbest = pop[j,:]\n",
    "            fitnessGbest=fitness[j]\n",
    "            \n",
    "    trace[i]=fitnessGbest\n",
    "    print(fitnessGbest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "received-punishment",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "above-nicaragua",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [-36.63196349],\n",
       "       [-61.66761388],\n",
       "       [-75.32492437],\n",
       "       [-73.91005596],\n",
       "       [-36.73924494],\n",
       "       [-52.81022088],\n",
       "       [-16.10009253],\n",
       "       [-62.04322852]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "actual-yeast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trace,label='wuhu')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "working-finance",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "numeric-portugal",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
